DecisionTree

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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11. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    

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14. def log: Logger

    

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15. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    

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16. def logDebug(msg: ⇒ String): Unit

    

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17. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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18. def logError(msg: ⇒ String): Unit

    

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19. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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20. def logInfo(msg: ⇒ String): Unit

    

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21. def logName: String

    

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22. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    

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23. def logTrace(msg: ⇒ String): Unit

    

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24. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    

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25. def logWarning(msg: ⇒ String): Unit

    

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27. final def notify(): Unit

    

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28. final def notifyAll(): Unit

    

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29. final def synchronized[T0](arg0: ⇒ T0): T0

    

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30. def toString(): String

    

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31. def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int, maxBins: Int, quantileCalculationStrategy: QuantileStrategy, categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel

    

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    Note: Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

    algo

    Type of decision tree, either classification or regression.

    impurity

    Criterion used for information gain calculation.

    maxDepth

    Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).

    numClasses

    Number of classes for classification. Default value of 2.

    maxBins

    Maximum number of bins used for splitting features.

    quantileCalculationStrategy

    Algorithm for calculating quantiles.

    categoricalFeaturesInfo

    Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

    returns

    DecisionTreeModel that can be used for prediction.

    Annotations

    @Since( "1.0.0" )

32. def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int): DecisionTreeModel

    

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    Note: Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

    algo

    Type of decision tree, either classification or regression.

    impurity

    Criterion used for information gain calculation.

    maxDepth

    Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).

    numClasses

    Number of classes for classification. Default value of 2.

    returns

    DecisionTreeModel that can be used for prediction.

    Annotations

    @Since( "1.2.0" )

33. def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int): DecisionTreeModel

    

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    Note: Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

    algo

    Type of decision tree, either classification or regression.

    impurity

    Criterion used for information gain calculation.

    maxDepth

    Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).

    returns

    DecisionTreeModel that can be used for prediction.

    Annotations

    @Since( "1.0.0" )

34. def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel

    

    Method to train a decision tree model.

    Method to train a decision tree model. The method supports binary and multiclass classification and regression.

    Note: Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

    strategy

    The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.

    returns

    DecisionTreeModel that can be used for prediction.

    Annotations

    @Since( "1.0.0" )

35. def trainClassifier(input: JavaRDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainClassifier

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainClassifier

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    @Since( "1.1.0" )

36. def trainClassifier(input: RDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    

    Method to train a decision tree model for binary or multiclass classification.

    Method to train a decision tree model for binary or multiclass classification.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}.

    numClasses

    Number of classes for classification.

    categoricalFeaturesInfo

    Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

    impurity

    Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy".

    maxDepth

    Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 5)

    maxBins

    Maximum number of bins used for splitting features. (suggested value: 32)

    returns

    DecisionTreeModel that can be used for prediction.

    Annotations

    @Since( "1.1.0" )

37. def trainRegressor(input: JavaRDD[LabeledPoint], categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainRegressor

    Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainRegressor

    Annotations

    @Since( "1.1.0" )

38. def trainRegressor(input: RDD[LabeledPoint], categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

    

    Method to train a decision tree model for regression.

    Method to train a decision tree model for regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels are real numbers.

    categoricalFeaturesInfo

    Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

    impurity

    Criterion used for information gain calculation. The only supported value for regression is "variance".

    maxDepth

    Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 5)

    maxBins

    Maximum number of bins used for splitting features. (suggested value: 32)

    returns

    DecisionTreeModel that can be used for prediction.

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    @Since( "1.1.0" )

39. final def wait(): Unit

    

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40. final def wait(arg0: Long, arg1: Int): Unit

    

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41. final def wait(arg0: Long): Unit

    

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